Learning in Autonomous Transportation Systems
Interdisciplinary Areas: | Data and Engineering Applications, Autonomous and Connected Systems, Smart City, Infrastructure, Transportation |
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Project Description
This project is at the intersection of machine learning, network science and intelligent transportation. The concepts of mean field control, decentralizable algorithms, network science and reinforcement learning will be used for problems in scheduling of vehicles, traffic signal control, congestion pricing, management of autonomous and human vehicles, etc. The work will be a combination of theoretical contributions in machine learning, algorithm development and testing in real-world intelligent transportation systems.
Start Date
03/01/2022
Postdoc Qualifications
Desired candidate would have PhD in ECE, CS, Stats, or related areas, with experience with both mathematical proofs as well as programming in machine learning. Top-tier papers related to machine learning will be preferred, and some experience in applications to intelligent transportation will be a big plus.
Co-Advisors